(2024) 22:301
Dwaraka et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03513-w

BMC Medicine

RESEARCH ARTICLE

Open Access

Unveiling the epigenetic impact of veganvs. omnivorous diets on aging: insightsfrom the Twins Nutrition Study (TwiNS)
Varun B. Dwaraka1*†, Lucia Aronica2†, Natalia Carreras‑Gallo1, Jennifer L. Robinson2, Tayler Hennings3,
Matthew M. Carter4, Michael J. Corley5, Aaron Lin1, Logan Turner1, Ryan Smith1, Tavis L. Mendez1,
Hannah Went1, Emily R. Ebel4, Erica D. Sonnenburg4, Justin L. Sonnenburg4,6,7 and Christopher D. Gardner2*

Abstract
Background Geroscience focuses on interventions to mitigate molecular changes associated with aging. Lifestylemodifications, medications, and social factors influence the aging process, yet the complex molecular mechanisms
require an in-depth exploration of the epigenetic landscape. The specific epigenetic clock and predictor effects
of a vegan diet, compared to an omnivorous diet, remain underexplored despite potential impacts on aging-related
outcomes.
Methods This study examined the impact of an entirely plant-based or healthy omnivorous diet over 8 weeks
on blood DNA methylation in paired twins. Various measures of epigenetic age acceleration (PC GrimAge, PC Pheno‑
Age, DunedinPACE) were assessed, along with system-specific effects (Inflammation, Heart, Hormone, Liver, and Meta‑
bolic). Methylation surrogates of clinical, metabolite, and protein markers were analyzed to observe diet-specific shifts.
Results Distinct responses were observed, with the vegan cohort exhibiting significant decreases in overall epige‑
netic age acceleration, aligning with anti-aging effects of plant-based diets. Diet-specific shifts were noted in the anal‑
ysis of methylation surrogates, demonstrating the influence of diet on complex trait prediction through DNA meth‑ylation markers. An epigenome-wide analysis revealed differentially methylated loci specific to each diet, providing
insights into the affected pathways.
Conclusions This study suggests that a short-term vegan diet is associated with epigenetic age benefits
and reduced calorie intake. The use of epigenetic biomarker proxies (EBPs) highlights their potential for assessing
dietary impacts and facilitating personalized nutrition strategies for healthy aging. Future research should explore
the long-term effects of vegan diets on epigenetic health and overall well-being, considering the importanceof proper nutrient supplementation.
Trial registration Clinicaltrials.gov identifier: NCT05297825
Keywords Diet and Nutrition, Epigenetic clocks, Aging, Epigenome-wide association study, Vegan, Omnivore†

Varun B. Dwaraka and Lucia Aronica contributed equally to this work.

*Correspondence:
Varun B. Dwaraka
varun@trudiagnostic.com
Christopher D. Gardner
cgardner@stanford.edu
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom‑mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Dwaraka et al. BMC Medicine(2024) 22:301

Background
While advances in technology and medicine have allowed
the average person to live longer, age-related disease andimpairment remain an issue that greatly impacts individuals and healthcare systems. Aging is associated withincreases in health care costs and financial stress onsocial insurance systems [1]. In light of these challenges,
the field of geroscience has emerged, proposing interventions aimed at slowing down or reversing the molecular changes that occur with aging. These interventions
encompass a wide range of factors, including lifestylemodifications, nutrition, medications, sleep, and social
factors, all of which can influence the aging process andpotentially delay or prevent the onset of multiple chronic
diseases, ultimately extending healthy lifespan [2–4].
Consequently, the exploration of nutritional and dietary
recommendations has become an increasingly significant
area of research within the broader field of aging, providing insights into how dietary choices can impact the
aging process and overall health outcomes.
However, unraveling the intricate molecular mechanisms through which diets influence aging necessitates
a deeper understanding of the epigenetic landscape [5].
Epigenetic modifications, such as DNA methylation, haveemerged as pivotal regulators of gene expression andprovide a promising avenue for investigating the effects
of vegan diets on the aging process [6]. The epigenetic
effects of a vegan diet, in comparison to an omnivore
diet, remain largely unexplored, with limited available
evidence. Although certain studies have indicated potential positive impacts of specific components of a vegandiet, such as heightened intake of vegetables and fruits,
on epigenetic aging, concerns have been raised regarding potential deficiencies in essential “epi-nutrients” necessary for effective epigenetic regulation [7]. Notably,
vitamins and nutrients, including vitamin B12, vitamin
B+, choline, vitamin D, omega-3 fatty acids, and zinc,
are among the concerns associated with a vegan diet, as
their availability may be compromised. Furthermore,other work on diets has aimed to discover the association between diets and longevity [8, 9]. For instance, theMediterranean diet has been documented to slow the
progression of frailty with aging [10]. Dietary protein
intake is another important factor considered in aging
and frailty, with many studies showing beneficial impacts
of protein regardless of animal or plant origin [11]. Theseand other studies have provided mixed notions of ahealthy vegan diet, necessitating additional interrogationof its impact on aging and disease outcomes, as measured
by aging markers.
Epigenetic clocks, derived from DNA methylation patterns, have emerged as powerful tools for estimating biological age and predicting age-related outcomes. These

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clocks have also been refined over time to incorporate
known clinical factors, making them sensitive and reliable indicators of aging-related changes [12]. Additionally,
epigenetic interpretation algorithms have proven valuable in predicting relative immune cell levels and protein expressions, providing insights into immune system
functionality through immune deconvolution [13–15].
Moreover, these clocks can estimate the number of cell
cycle divisions, reflecting cellular senescence and potential disease susceptibility [16].
While aging intervention studies face the challenge ofrequiring sufficiently long periods to show statisticallysignificant effect, advancements in DNAm-based analysis, such as phenotypically and clinically trained DNAmclocks, have allowed for changes in the pace of aging andrisk factors related to aging to be studied [17]. Epigenetic
age trials using these epigenetic clocks have found thatdifferent diets such as a Mediterranean diet and DASH
diet have shown improvements of aging pathways andmarkers, including protective effects of immunosenescence markers, activation of mTOR pathway, and epigenetic aging [18, 19]. In particular, a Mediterranean diethas been shown to both slow aging and delay the onset offrailty [20].
Given the discussion on which diets are most beneficial
to longevity, this study aims to identify the effect of an
8-week plant-based or healthy omnivorous diet on blood
DNA methylation in twins and evaluate age-related risk
factors and health biomarkers. The novelty of this study
includes the twin-pair study design which controls forgenetic, age, and sex differences, while highlighting themethylation changes based on diet. Furthermore, this is
the first study assessing the impact of epigenetic measures on twin-pair study design, and specifically addressed
whether diet impacts such measures. Finally, we conducted a differential methylation analysis using thetwin-pair design to identify potential DNAm markers
which are related to the application of a healthy vegan oromnivorous diet, while also identifying DNAm markers
which differentiate between diets. This comprehensive
approach will provide insights into how diet type influences epigenetic dynamics and contribute to our understanding of potential interventions in the process ofnutrition.

Methods
Ethical approval and study design

Procedures adhered to the ethical standards of the Helsinki Declaration, approved by the Stanford University Human Subjects Committee (IRB protocol 63955,
approved March 9, 2022). Written informed consent was
obtained from all participants. The study, a single-site,parallel-group dietary intervention trial, randomized

Dwaraka et al. BMC Medicine(2024) 22:301

generally healthy adult twins to either a healthy vegan oromnivorous diet for 8 weeks. Enrollment commenced in
March 2022, concluding in May 2022, with the final follow-up in July 2022. The trial employed the CONSORT
reporting guideline for randomized clinical trials, focusing on the primary outcome: the 8-week change in DNA
methylation profiles from baseline. Secondary outcomes
encompassed triglycerides, HDL-C, glucose, insulin,
TMAO, vitamin B12, and body weight, serving as controls for relevant methylation risk scores and were published previously [21]. Diet quality, adherence, and study
design are illustrated in Fig. 1.
Participant recruitment and eligibility

The goal was to recruit 22 pairs of identical twins—controlled for sex, age, and ethnicity—primarily from the
Stanford Twin Registry and other twin registries, including Netflix’s pre-recruited participants interested in adocumentary on vegan diets. Inclusion criteria involved
participants aged ≥18, part of a willing twin pair, with
BMI <40, and LDL-C <190 mg/dL. Exclusions included
uncontrolled hypertension, metabolic disease, diabetes,
cancer, heart/renal/liver disease, pregnancy, lactation,and medication use affecting body weight or energy. Eligibility was determined via online screening, followed byan orientation meeting and in-person clinic visit. Randomization occurred only after completing baseline visits, dietary recalls, and questionnaires for both twins.
One twin pair (which started the study, did not abide by
the above requirements and thus was removed from thestudy. Ultimately blood samples from 21 numbers of twinpairs (N = 42) were considered for downstream analyses.
Full details of the participant profiles are detailed in Landry et al. 2023 [21].

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Dietary intervention and lifestyle changes

The study comprised two 4-week phases: delivered meals
and self-provided meals. Trifecta Nutrition supplied
meals for the first 4 weeks, tailored to omnivorous andvegan diets. Health educators facilitated nutrition classes
via Zoom, emphasizing principles like choosing minimally processed foods and building balanced plates. The
omnivorous group received animal product targets (e.g.,
6–8 ounces of meat, 1 egg, and 1.5 servings of dairy),while the vegan group avoided all animal products. Dietary intake was assessed through unannounced 24-hrecalls and participant logs on the Cronometer app, capturing food intake details at baseline, week 4, and week 8.
Dietary data quality was ensured through trained dietitian interviews and app records, used to evaluate dietquality and adherence. To account for lifestyle changes,
participants filled out surveys on global health, fatigue,
stress, and physical activity, at baseline and week 8. Participants exhibiting notable changes in any of these factors were not considered in the analyses.
PCR‑based telomere estimation

DNA was extracted from whole blood stored at −80 °C
with the QIAamp DNA blood mini kit (QIAGEN cat#
51106). Relative telomere length was measured by quantitative polymerase chain reaction (qPCR), expressed as
the ratio of telomere to single-copy gene abundance (T/Sratio) [22, 23]. A detailed protocol can be found on the
Telomere Research Network’s website (https://​trn.​tulane.​
edu/​wp-​conte​nt/​uploa​ds/​sites/​445/​2021/​07/​Lin-​qPCR-​
proto​col-​01072​020.​pdf ). The inter-assay coefficient ofvariation (CV) for this study is 2.0%±1.7%. The baseline
and follow-up samples of the same participant were processed in the same batch throughout the whole assay.

Fig. 1 Timeline diagram for the study design. A total of 21 pairs of twins (N=42) were subjected to a vegan diet (N = 21, labeled in green)
and an omnivore diet (N = 21, labeled in orange). Blood was collected for baseline at the start of the trial (week 0) and at the end of the trial (week8) and methylation states were quantified using the EPIC 850k array

Dwaraka et al. BMC Medicine(2024) 22:301

Lab personnel is blind to all the demographic and clinicaldata.
DNA methylation assessment

Whole blood was collected at baseline and at week 8
for DNA methylation preparation and analysis. Majority of twin pairs (20 twin pairs, N = 40) were collected as
biological replicates per time point and individual using
Dried Blood Spot cards; one twin pair (N=2 patients)
which had triplicate collections in which two were collected by dried blood spot and one using the tasso. Bloodcollected by the clinics was sent to TruDiagnostic labs
in Lexington, KY, for DNA extraction and methylation
processing. Using the EZ DNA Methylation kit (Zymo
Research), 500 ng of DNA was bisulfite-converted following the manufacturer’s instructions. Bisulfite-converted
DNA samples were randomly assigned to wells on the
Infinium HumanMethylationEPIC BeadChip, and thesubsequent steps included amplification, hybridization,
staining, washing, and imaging with the Illumina iScan
SQ instrument to acquire raw image intensities. Longitudinal DNA samples for each participant were assessedon the same array to mitigate batch effects. Raw image
intensities were saved as IDATs for further processing.
DNAm data processing

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calculated using a custom script available from Github(https://​g ithub.​com/​d anbe​l sky/​D uned​i nPACE, [35]).
Additional non-epigenetic age metrics included relative
percentages of 12 immune cell subsets imputed using
EpiDISH [15], 116 methylation-based predictions of biochemical and lifestyle risk factors using MethylDetectR[36], and 396 epigenetic biomarker proxies [14]. All epigenetic metrics such as clocks, telomere length, immune
deconvolution, EpiScore, and EBPs, were residualizedprior to statistical analysis by using the lmer() R package
as such:Residualized epigenetic metric = resid(lmer(Epigenetic
predictor ~ Chronological Age + Sex + PC1 + PC2)

Estimates of EpiScores and EBPs were calculated using
multivariate models described previously. Briefly, theseestimates were derived by modeling DNAm beta values to predict relative protein estimates, as quantified
by Olink and SEER/Mass Spec; metabolite estimates, as
quantified by the Metabolon panel; clinical values; andclinical and laboratory protein estimates collected fromvarious clinics and panels [14, 37]. Resulting scores andestimates were then used for statistical analyses. All comparisons utilized paired Wilcoxon-rank sum tests facetedby diet type, with significance set at unadjusted p < 0.05.

Raw IDATs underwent processing using the minfi pipeline [24]. Samples of low quality were identified with
ENMix based on variance of internal controls, flagging
samples showing more than 3 standard deviations awayfrom the mean control probe value [25]. However, no
outlier samples were identified, and thus, all samples wereconsidered for analysis. DNAm normalization involves
Gaussian mixed quantile normalization (GMQN) tocorrect between batch collections and BMIQ normalization to normalize intra-sample variance within chips[26]. Probe-level analysis masked probe sets without atleast one intensity fluorescence above the backgroundas implemented by pOOBAH. Missing beta values were
imputed using K nearest neighbor (KNN) imputation.

Assessment of concordance for DNAm and surrogate
valuesDeriving estimates of epigenetic clocks
and methylation‑based metrics

Differential methylation analysis was conducted using
processed beta values logit-transformed to M-values
with the BetaValueToMValue function from the sesame
R package. No additional probes (e.g., sex associatedprobes) were pre-filtered in order to prior to the analysis. However, technical variation and sex were consideredin the final model for differential methylation. Limma
package was applied across the four comparisons: withinvegan (week 0 vs. week 8), within omnivore (week 0 vs.
week 8), cross-sectional Vegan vs. Omnivore (at week8) and cross-sectional Vegan vs. Omnivore (week 0, orbaseline). Differentially methylated loci (DMLs) were

Epigenetic clocks were calculated from cleaned beta values, focusing on clocks like Horvath multi-tissue [27],Horvath skin and blood [28], Hannum [29], PhenoAge[30], GrimAge v1 and v2 [31, 32], and DNAmTL [33]. To
ensure that values were highly reproducible, the principal component versions of these clocks were utilized as
described by Higgins-Chen et al. [12]. Individual systems
clocks were calculated using the framework presented
by Sehgal et al. [34]. Clocks were calculated using a custom R script available on Github. DunedinPACE was

Analyses of telomere and BMI values performed between
the reported clinical/qPCR values and DNAm predictedvalues were conducted in R. Values were scaled using
the scale() function prior to comparison. Cohen’s d statistics were calculated by inputting scaled values into thecohen.d() function available in the effsize library. Statistical significance was assessed using paired Wilcoxonrank sum tests implemented in the wilcox.test() package.
Spearman correlations and associated p-values were calculated using the cor.test() package in R and setting themethod = “spearman”.
Differentially methylated analysis

Dwaraka et al. BMC Medicine(2024) 22:301

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identified using different modeling types based on comparison. For within diet comparisons which were longitudinal, multivariate linear models were controlled for
fixed effects such as chronological age, BMI, sex, beadchip, 5 immune cell percentages (basophils, CD8T naive,eosinophils, NK, and Neu), the first three principal components of technical probes. For the cross-sectional comparisons, the same fixed effects and PC components wereused; however, the individual ID was used in the longitudinal comparison. The inflation or deflation of P-values
across the methylome was assessed with Q-Q plots andlambda values [38]. DMLs were identified with a significance level of unadjusted p < 0.001. False discovery rate(FDR) were also calculated as implemented within thelimma package and reported.
GREAT analysis

Functional annotation of DMLs was performed using
the GREAT pipeline to identify significant gene ontology terms, as implemented in the rGREAT R package[39]. Significant enrichment terms were identified usinga Hyper_Raw_PValue < 0.0001; however, only those passing a correct p-value (FDR < 0.05) were discussed.

Results
Description of study population

To investigate the impact of diet on the methylome,
blood samples from a randomized clinical trial wereused to quantify methylation [21]. As shown in Fig. 1,
to quantify methylation, whole blood was collected toestablish a baseline measure of methylation at the timeof starting the trial (week 0) and at the conclusion of thestudy (week 8). Baseline characteristics by diet group
appear in Table 1. Among 21 pairs of twins, the randomized mean age was 39.9 (SD 13) years, 77.3% were
women, and the mean body mass index was 26 (SD 5).
The BMI of both cohorts were largely equivalent due toeach group matched to paired-twins with equivalent BMI
and genetic makeup (average Vegan BMI = 26.3, average
Omnivore BMI = 26.2). The paired-twin design developed here is unique as it controls for genetic and physiological differences between individuals surveyed, which
ultimately increases the power of statistical comparisons
Table 1 Baseline characteristics of the study participants
VegansOmnivores

Sample size (N)2121Males (%)

5 (18.5%)5 (18.5%)

Females (%)

16 (76.2%)

16 (76.2%)

Mean age (SD)39.9 (13)39.9 (13)Mean BMI (SD)26.3 (5)26.2 (5)across the two groups. Full descriptions and characteristics of the study population are detailed previously [21].
Diet type impacts changes in epigenetic age

To investigate the response to diet on biological age andtelomere length, we quantified and analyzed several
biological age and telomere length predictors derived
from DNAm. These included the principal component(PC)-based clocks: the first-generation multi-tissue Horvath (Horvath1) and skin+blood Horvath (Horvath2);and the second-generation PhenoAge, GrimAge, andDNAm telomere clocks. Additionally, several non-PC
clocks were included as well: the first-generation Zhang
clock based on the elastic net (Zhang-EN) and BLUP
(Zhang-BLUP) method; the second-generation multiomic informed OMICmAge, and the third generation
DunedinPACE clock. To better understand the impact ofdiet on the epigenetic age of specific organ systems, we
also calculated the individual ages of 11 organ systems:
Heart, Lung, Kidney, Liver, Brain, Immune, Inflammatory, Blood, Musculoskeletal, Hormone, and Metabolic.
In addition, a composite age of the system was also calculated as Systems Age. In the vegan group, we observed
significant decreases in the following epigenetic age metrics: PC GrimAge (mean Δ EAA = −0.3011, p = 0.033),
PC PhenoAge (mean ΔEAA = −0.7824, p = 0.014), andDunedinPACE (mean Δ PACE residual = −0.0312, p =0.00061) significantly decreased at 8 weeks relative to 0
weeks (Fig. 2A–C). Similarly, we observed significant
reductions in the composite systems age metric, which
was corroborated by significant reductions of 5 out of
11 systems: Inflammation, Heart, Hormone, Liver, andMetabolic (F igure 2D–I). In contrast, no epigenetic clock
or telomere measure exhibited significant changes in theomnivorous cohort, suggesting that the omnivorous diet
did not induce any epigenetic age methylation changes.
Taken together, these findings suggest that the observed
DNA methylation changes may contribute to the overall
decreases in epigenetic age in response to a vegan diet,which is not observed among omnivores.
Telomere length quantified by Telomere Shortening Rate
(TSR) exhibits changes, but not epigenetic telomere length

We next sought to elucidate the potential impact of vegan
diets on telomere length. First, we established an understanding of concordance between estimated telomere
length using quantitative polymerase chain reaction(qPCR) and the estimated PC DNAmTL values by quantifying the correlations between the values of all samples,and assessing significant differences agnostic to groups.
Overall, we observed an overall correlation > 0.5 between
the scaled qPCR and PC DNAmTL values calculated(⍴ = 0.564; p < 1.22e−7), and no significant differencesDwaraka et al. BMC Medicine(2024) 22:301

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Fig. 2 Boxplot showing the evolution of epigenetic age acceleration (EAA)/residuals among the significant epigenetic age clocks
and systems-specific clocks based on diet type. Clocks assessed include the A PC GrimAge, B PC PhenoAge, C DunedinPACE, D Systems Age, E
Inflammation Age, F Heart Age, G Liver Age, H Metabolic Age, and I Musculoskeletal Age. On the X-axis, the time points of measurements in weeks.
On the Y-axis, the EAA/residual measure. EAA, or residual, is defined as the residual calculated between the raw value regressed upon chronologicalage, and adjusted by sex, technical principal components 1 and 2. On the top, the mean and median values of the EAA at each time point. The
p-values of the paired Wilcoxon-rank sums test are also displayed in the plots. Lines that connect both boxplots represent the average of eachpatient’s tests(Wilcoxon-rank sum p = 0.9427). This was further supported by a negligible effect size difference between thescaled values (Cohen’s d estimate = −2.45e−15, 95% CI =−0.321–0.321). These results suggest that the values generated by both methods are comparable.
Next, changes in TSR were assessed between timepoints within each diet among the TSR data. The average telomere length was significantly longer at week
8 than at week 0 for Vegans (p = 0.045, Δ T/S ratio =0.0361, Fig. 3A) but not for omnivores (p = 0.86, Δ T/S
ratio = −0.0045, Fig. 3A). Furthermore, paired analyses
comparing twins between diets within each time point
were conducted among the TSR samples, which foundthat the Vegan group had significantly longer telomeres
than their Omnivore twins at week 8 (p = 0.01, Δ T/S
ratio = 0.042) but not at week 0 (p = 0.54, Δ T/S ratio= 0.0013), further confirming that the telomere extension was specific to the vegan diet. This contrasted

the findings observed among the PC DNAmTL values,
which showed that there were no significant changes
between week 8 and week 0 measures among the Vegan
or Omnivore cohort (Fig. 3B).
Analysis of cell cycle changes shows no significant changes
based on dietWe next assessed whether diet type exhibited differences in overall mitotic rate as quantified by the
mitotic clock output. Using the epiTOC2 algorithm, weobserved no significant changes in either the vegan oromnivore diet when assessing the total number of stem
cell replication cycles estimated (tnsc) or the intrinsic
stem cell cycle rate based on tissue (irS). This suggeststhat diet type did not have an impact on the overall
mitotic clock values from the data.

Dwaraka et al. BMC Medicine(2024) 22:301

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Fig. 3 Boxplot showing the change between relative telomere levels as quantified by qPCR and DNA methylation (PC DNAmTL). Telomere qPCR
value is reported in panel A, while the PC DNAmTL values are reported in B. On the X-axis, the time points of measurements in weeks. On the Y-axis,
the T/S ratio is shown for qPCR, or the residual of PC DNAmTL. The PC DNAmTL residual is defined as the residual calculated between the raw PC
DNAmTL value regressed upon chronological age, and adjusted by sex, technical principal components 1 and 2. On the top, the mean and mediansof the Y-axis values at each time point are reported. The p-values of the paired Wilcoxon-rank sums test are also displayed in the plots. Lines
that connect both boxplots represent the average of each patient’s tests

Vegan diets exhibit significant changes in relative basophil
levels

The immune system undergoes distinctive changes basedon dietary choices, with vegan and omnivore diets influencing immune cell behavior in unique ways. Exploring
this interplay provides valuable insights into the intricate relationship between diet and the body’s immune
defenses. To investigate the impact of diet on the
immune system, we next analyzed relative immune cellsubset changes throughout the trial among 12 immune
cell subsets quantified by the EPIDISH frame: CD8Tnaive, CD8T-memory, CD4T-naive, CD4T-memory,basophils, B naive, B memory, T-regulatory, monocytes,
neutrophils, natural killer, and eosinophils. We observed
significant changes in basophil levels in the vegan andomnivore diets. However, the basophil levels increased in
the vegan group (Δ mean = 0.0014, p = 0.04, Fig. 4) anddecreased in the omnivore group (Δ mean = −0.0018, p= 0.048).
Assessment of type 2 diabetes risk based on lociPrevious studies have shown plant-based diets associated with reduced type 2 diabetes risk [40, 41]. To test
whether epigenetic changes are consistent with previous
findings, we analyzed two DNA methylation loci, ABCG1(cg06500161) and PHOSPHO1 (cg02650017), which are
implicated in predicting T2D risk [42]; increased methylation in ABCG1 correlates with a higher T2D risk,
while heightened PHOSPHO1 methylation is linked to a
reduced risk. In our study, the vegan group displayed asignificant increase in methylation at the ABCG1 loci (Δbeta value mean = 0.0105, p = 0.0093, Fig. 5A), indicating
a potentially elevated T2D risk. Concurrently, an increasein PHOSPHO1 cg02650017 methylation (Δ beta valuemean = 0.0079, p = 0.011, Fig. 5B) suggests a decreased
T2D risk for the vegan cohort. This dichotomy in methylation changes for the two loci within the vegan group
underscores a complex relationship between diet andT2D biomarkers, necessitating further investigation for acomprehensive understanding. None of these CpG sites
were differentially methylated over time in the omnivore
group.
Analysis of EpiScore markers

Recent efforts have expanded DNA methylation proxies to predict proteins, complex behavioral, and physiological traits [13, 14, 26]. To this end, we utilized
these DNAm-based surrogate markers to assess relative changes in response to diet type (Additional file 1:
Table S1). In an initial analysis, we utilized EpiScore

Dwaraka et al. BMC Medicine(2024) 22:301

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Fig. 4 Boxplots showing the evolution of basophil cell subset percentages based on diet type. On the X-axis, the time points of measurements
in weeks. On the Y-axis, the basophil measure. The basophil measure is residualized, which is defined as the residual of the raw deconvolution
value regressed upon chronological age, and adjusted by sex, technical principal components 1 and 2. On the top, the mean and medianvalues of the residual at each time point. The p-values of the paired Wilcoxon-rank sums test are also displayed in the plots. Lines that connect
both boxplots represent the average of each patient’s tests

Fig. 5 Boxplots showing the relative beta value change of two weight loss methylation sites on the ABCG1 gene (reported on the left)
and PHOSPHO1 gene (reported on the right). On the X-axis, the time points of measurements in weeks, and the loci beta value which is reported
on the Y-axis. On the top, the mean and median beta values of the loci at each time point. The p-values of the paired Wilcoxon-rank sums test arealso displayed in the plots. Lines that are connecting both boxplots represent the average of each patient’s tests

Dwaraka et al. BMC Medicine(2024) 22:301

models previously described: multivariable linear models of beta values used to predict the estimates of the 116
modeled proteins, behavioral and physiological traits[26]. Comparison of EpiScore values between time 8
and time 0 samples detected significant changes in seven
EpiScores in the Vegan group, using a unadjusted p<0.05:
CCL21, MMP1, ENPP7, Testican 2, ADAMTS, CD163,
and MMP2. Notably, these EpiScores were not evident in
the omnivore group analysis, underscoring diet-specificvariations. Conversely, in the omnivore-specific analysis,
six EpiScores—Ectodysplasin A, PAPP-A, VEGFA, HGF,
Body Fat %, and TNFRSF17—exhibited exclusive significant change at an unadjusted threshold of p < 0.05. However, it must be noted that in both analyses, none of the
EpiScore values met a multi-comparison corrected significance threshold (adjusted BH, p < 0.05). In summary,the methylation-based surrogate markers of complex
physiological and behavioral traits identified here suggest that while common markers are present, the majority of changes among the EpiScore are unique among diettypes.
Analysis of Epigenetic Biomarker Proxies (EBP)

We also assessed changes in EBPs: DNAm proxy scoresof metabolites, proteins, and clinical values estimatedusing multivariate linear models composed of DNA
methylation values that were previously described [14].
Of the 396, we identified a total of 76 and 89 EBPs whichshowed significant changes among the vegans and omnivores, respectively, using an unadjusted p < 0.05 (Additional file 2: Table S2). After correcting for multiple
comparisons (BH < 0.05), 13 and 19 EBPs satisfied theadjusted threshold. In the following independent analyses were performed between vegan and omnivore diets
respectively to identify EBPs which showed (1) uniquechanges among diet types, (2) consistent changes among
diet types, and (3) opposing changes among diet types.
We identified 33 EBPs that showed uniquely significant changes (unadjusted p < 0.05) among the vegancohort: androsterone glucuronide, homovanillate (HVA),
branched-chain, straight-chain, or cyclopropyl 10:1
fatty acid (2)*, Liver albumin, CCL18, PON1, dehydroepiandrosterone sulfate (DHEA-S), PON1, glutamine_degradant, leucine, 1,5-anhydroglucitol (1,5-AG), CRP,
arabitol/xylitol, retinol (vitamin A), 3-hydroxyindolin2-one sulfate, 2-methylcitrate/homocitrate, deoxycholicacid glucuronide, 7-hydroxyindole sulfate, alpha-CMBHC
glucuronide, PCOC1, riboflavin (vitamin B2), 1-palmitoyl-GPC (16:0), PCOC1, GRN, S-carboxyethylcysteine,
FETUA​, CSPG2, dimethyl sulfone, carotene diol (2), guanidinosuccinate, 6-oxopiperidine-2-carboxylate. Amongthese, 3 EBPs - androsterone glucuronide, homovanillate(HVA), branched-chain, straight-chain, or cyclopropyl

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10:1 fatty acid (2)* - further passed an adjusted p-value
threshold (BH < 0.05), suggesting that the EBPs identified
here represent potential biomarkers uniquely altered in
response to a vegan diet at 8 weeks.
Among omnivores, we observed 46 EBPs which showedsignificant changes only among the omnivore diet cohort:
4-methoxyphenol sulfate, N-methylpipecolate, N-acetylcitrulline, sucrose, vanillactate, uridine, N-acetyltyrosine,
3-hydroxybutyroylglycine, Liver_ALP, tryptophan, dihydroferulic acid sulfate, salicyluric glucuronide*, picolinate, 3,5-dichloro-2,6-dihydroxybenzoic acid, urea,
galactonate, thyroxine, 2-acetamidophenol sulfate, cystathionine, sphinganine-1-phosphate, choline phosphate,picolinoylglycine, N,N,N-trimethyl-5-aminovalerate,
1-pentadecanoyl-GPC (15:0)*, TLL1, PCOC1, glycochenodeoxycholate 3-sulfate, trans-4-hydroxyproline, gentisate,catechol glucuronide, citramalate, ferulic acid 4-sulfate,
PLMN, sedoheptulose, vanillic acid glycine, PCOC1,
BMP1, linoleoylcarnitine (C18:2)*, 1-methylguanidine,
isobutyrylcarnitine (C4), indolebutyrate, hypoxanthine,
Smoking_PackYears, 3-hydroxyoctanoylcarnitine (1),eicosenoylcarnitine (C20:1)*, and BMP1. Among these, 8
EBPs passed an adjusted p-value threshold (BH < 0.05),
with 4-methoxyphenol sulfate, N-methylpipecolate,N-acetylcitrulline, sucrose, vanillactate, uridine, andN-acetyltyrosine exhibiting a significant increase among
the omnivore group at week 8, and a significant decreasein uridine and 3-hydroxybutyroylglycine. These EBPs represent biomarkers uniquely associated with the omnivore
diet but not vegan diet.
We also identified several EBPs which showed consistent changes among the diet types. Approximately 16 of
the vegan EBPs showed significant increase in both veganand omnivore diet types which included CCL16, glucuronide of C12H22O4 (2)*, 2-methoxyhydroquinone sulfate(1), adenosine, lactosyl-N-palmitoyl-sphingosine (d18:1/16:0),
1-stearoyl-2-dihomo-linolenoyl-GPC (18:0/20:3n3 or 6)*,N-acetylalliin, N-carbamoylalanine, caffeine, carnitine,
1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4)*, FETUA​, 2,3-dihydroxy-2-methylbutyrate, LYSC, eicosenedioate(C20:1-DC)*, and 1-methyl-5-imidazoleacetate). Conversely, approximately 21 exhibited decreases among both
diets, which included 10-undecenoate (11:1n1), 1,2-dipalmitoyl-GPC (16:0/16:0), 3-carboxy-4-methyl-5-propyl2-furanpropanoate (CMPF), salicylate, succinylcarnitine(C4-DC), 1-margaroyl-2-arachidonoyl-GPC (17:0/20:4)*,
5-methyluridine (ribothymidine), Glucose, 2-aminoheptanoate, stearoyl-arachidonoyl-glycerol (18:0/20:4) [1]*,PCOC1, proline, ibuprofen, 11-ketoetiocholanolone glucuronide, homoarginine, Triglyceride, PCOC1, PCOC1,
1-stearoyl-2-adrenoyl-GPC (18:0/22:4)*, BMI, and3-hydroxyphenylacetoylglutamine). These EBPs represent
surrogate markers of metabolite, clinical, and proteinsDwaraka et al. BMC Medicine(2024) 22:301

which changed regardless of diet type, suggesting theseas non-diet associated EBP markers.
However, we observed 6 EBPs which showed opposingchanges in EBP levels: serine, 1-margaroyl-GPE(17:0)*,
and 4-acetamidophenol, showed significant increases
among vegans, and significant decreases among omnivores, while ergothioneine, indoleacetylglutamine, andcreatinine showed a significant decrease among vegans
compared to the increase observed among omnivores.
The significant, and opposing, changes between dietssuggest that these represent diet-based interactions significant in one diet but not the other.
Assessing congruence among BMI and BMI‑EBP measures

To better assess the reproducibility of the EBPs calculating clinical measures, we compared the BMI-EBP
changes relative to the BMI-clinical values that were collected within this study. First, we assessed the correlation
between all BMI-clinical values with the BMI-EBP counterparts, which resulted in significant correlations among
the values (⍴ = 0.275, p = 0.0022) and negligible difference in mean difference (Cohen’s d = 1.10e−16, 95% CI= −0.252–0.252) after scaling. Analysis of the longitudinal data identified that both BMI measurements showed
consistent significant decreases in both diet types (p <Page 10 of 190.05). However, the magnitude of change was higher inthe BMI-clinical values compared to the BMI-EBP values(Fig. 6). Taken together, these findings exhibit the reproducibility of the BMI metrics among the EBPs relative totheir clinical counterparts.
Global EWAS analysis identifies epigenetic markers
of vegan and omnivorous diets

We utilized an exploratory epigenome-wide analysis
approach of 866,836 CpGs to identify candidate differentially methylated loci associated with a vegan oromnivore diet. To run the correct EWAS model foreach comparison, we first tested for test-statistic inflation (lambda) with each EWAS model adjusted by different fixed effects [27]. The final models ultimately chosenreported lambdas closest to 1 in each of the comparison:
within vegan (week 0 vs week 8) lambda chosen is 0.97;
within omnivore (week 0 vs week 8) lambda chosen is0.89; cross-sectional week 8 comparison lambda chosen
is 1.03; and cross-sectional week 0 comparison chosen is
1.06.
Utilizing the optimal EWAS models, differentiallymethylated loci DMLs were identified. In the first comparison, we identified a total of 607 differentially methylated loci (DMLs) associated with 8 weeks of a veganFig. 6 Boxplot showing the evolution of BMI values calculated from clinical measures (reported on the left) and epigenetic biomarker proxy(EBP) measures (reported on the right). On the X-axis, the time points of measurements in weeks. On the Y-axis, the BMI measure. The BMI-EBP
measurements are reported as residuals, which are defined as the residual of the raw BMI value regressed upon chronological age, and adjusted
by sex, technical principal components 1 and 2. No residual calculation was done for the clinical EBP. On the top, the mean and median valuesof the BMI at each time point. The p-values of the paired Wilcoxon-rank sums test are also displayed in the plots. Lines that connect both boxplots
represent the average of each patient’s tests

Dwaraka et al. BMC Medicine(2024) 22:301

diet (p-value < 0.001) compared to week 0 (Fig. 7A).
Among these vegan-diet associated loci, 322 CpG sites
showed hypomethylation at 8 weeks, and 312 loci exhibited hypermethylation at week 8. Among the omnivore
cohort, a total of 494 DMLs were associated with 8 weeks
of an omnivore diet (p-value < 0.001) (Fig. 7B), in which
309 CpGs showed increases in DNA methylation and 185
CpGs exhibited loss in DNA methylation at week 8. The
full list of DMLs associated with 8 weeks of a vegan oromnivore diet is listed in Additional file 3: Tables S3 andS4 for both analyses. The DMLs identified here represent
potential methylation markers of specific dietary interventions in response to the consumption of vegan diet oromnivorous diet, respectively.
To better understand the specific DNA methylation patterns that differentiated vegan diet samples andomnivorous diets, a cross-sectional analysis comparing
these groups at the week 8 time points was conducted.
We identified a total of 980 DMLs that were differentiallymethylated between the participants on an omnivore dietat week 8 and the participants on a vegan diet at week 8(p-value < 0.001). Of the DMLs identified, 317 exhibited
hypermethylation in the week 8 vegan samples relative tothe week 8 omnivore samples, while 663 DMLs exhibited
hypomethylation in the week 8 vegan sample (or greater

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methylation in the omnivore group) (Fig. 8, Additional
file 3: Table S5). Similarly, a cross-sectional analysis atweek 0 was also conducted to identify the base difference
in methylation between the vegan and omnivore twins at
the time of starting the trial. A total of 834 DMLs wereidentified between the diets at week 0, with 385 hypermethylated loci in the vegan samples compared to theomnivores (average logFC difference of 0.498), and 452
hypomethylated DMLs (or greater in omnivores compared to vegans) exhibiting an average logFC differenceof −0.355. Baseline DMLs represent methylation differences of twins at their base and are reported in Additional file 3: Table S6.
Finally, to answer if the CpGs identified at week 8 wereuniquely differentially methylated to the identified week
0 DMLs, we compared the cross-sectional comparison
lists. Only 2 CpGs (cg04227789, cg18301717), or 0.2%,
overlapped in both comparisons, suggesting that the
DMLs identified at week 8 are likely due to a diet-based
effect.
Gene ontology pathway analysis of diet‑associated DMLs

To better understand the transcriptionally relevant biological processes associated with methylation changes,
gene ontology (GO) enrichment analyses were conducted

Fig. 7 Manhattan plots for the vegan and the omnivore epigenome-wide association studies. The Manhattan plot illustrates genes associatedwith CpG sites identified in the A vegan and B omnivore comparison, with each dot representing a CpG site and its vertical position corresponding
to the negative logarithm (base 10) of the unadjusted p-value for DNA methylation association (significance set at p = 0.001). The x-axis denotes
genomic positions organized by chromosomes, with color-coded dots indicating specific chromosomes, and prominently peaked dots represent
significantly associated CpG sites surpassing the genome-wide significance thresholdDwaraka et al. BMC Medicine(2024) 22:301

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Fig. 8 Volcano plot of DMLs identified in the comparison between vegan and omnivore diet at the week 8 time point. The volcano plot
illustrates DMLs identified in the Vegan vs. Omnivore comparison, with each dot representing a CpG site and its vertical position corresponding
to the negative logarithm (base 10) of the unadjusted p-value for DNA methylation association. The x-axis denotes the relative log fold change(logFC) of the m-values between the vegan and the omnivore diets. Values greater than 0 represent CpGs with greater methylation among vegans(blue), compared to the negative values which represent greater methylation among omnivores (red)among DMLs identified in the week 8 vegan and omnivore comparison. To ensure transcriptional relevance,
DMLs were inputted into the GREAT software by direction of methylation and CpGs overlapping with cisregulatory or other regulatory regions were linked togenes and thus assessed for their relationship to various biological processes (BP), molecular function (MF),and cellular component (CC) associations. Significantly
hypermethylated DMLs in the vegan group, or hypomethylated among omnivores, were reported as significantly enriched for GO-BP terms such as paracrine
signaling, response to beta-amyloid, neuron apoptosis,
and several developmental GO-BP terms (adjusted BH
p-value < 0.05). In addition, molecular function (GOMF) terms such as Ras guanyl-nucleotide exchange factor

activity were enriched for sites that exhibited significant
hypermethylation among the vegans, and lower among
the omnivores (adjusted BH p-value < 0.05, Additional
file 4: Table S7). CpGs that were hypermethylated among
omnivores, and hypomethylated among vegans, wereenriched for GO-BP terms associated with cell cycle(negative regulation of the G0-G1 transition), genomic
imprinting (regulation of gene expression by genetic
imprinting), cytosolic calcium ion transport, and cellularresponse to alcohol. Cell cycle and transcriptional activity were further supported by the enrichment of GO-MF
terms associated with RNA polymerase activity andtranscriptional processes (protein phosphatase inhibitor activity, RNA polymerase II regulatory region DNA
binding, and promoter-specific chromatin binding). Full

Dwaraka et al. BMC Medicine(2024) 22:301

results for biological associations to CpGs differentiallymethylated between diet types are listed in Additional
file 4: Table S8. In summary, the significant gene ontology terms identified reveal distinct associations with key
biological processes and molecular functions, shedding
light on the epigenetic mechanisms altered in response todietary choices.

Discussion
In this study, we sought to elucidate the impact of a“healthy vegan” or a “healthy omnivorous diet” on epigenetic age, telomere length, immune cell subsets, and type2 diabetes (T2D) risk-associated CpGs, building on current knowledge of nutrition on both diets. Our findings
reveal distinct responses to vegan and omnivore diets,aligning with existing literature on the subject. Notably,
the vegan cohort exhibited a significant decrease in epigenetic age acceleration, as demonstrated by reductions
in multiple epigenetic aging clocks, all of which weretrained upon clinical [28] and phenotypic scores (PCGrimAge, PC PhenoAge, 28-31). The usage of systemsspecific aging predictors further specified which organsystems showed age improvements, resolving five specific systems that showed aging improvements among thevegan cohort and not omnivores. These findings are consistent with previous research highlighting the potential
anti-aging effects of plant-based diets, known for theirrich antioxidant content and anti-inflammatory properties [43–45]. However, the significant impact of basophils
in the vegan group contrasts with studies emphasizing
the immunomodulatory benefits of plant-based diets,
suggesting that further investigation into the nuanced
interactions is warranted [46]. These comprehensive
findings underscore the complex interplay between diet,
epigenetic regulation, immune function, and metabolic
health, offering valuable insights for future research andpersonalized health interventions.
The measures investigated in our study offer a holistic
perspective on biological aging without isolating systemspecific aging processes, as highlighted by Ahadi in 2020[47]. However, the incorporation of the systems age clockin our research addresses this limitation by providing
valuable, system-specific insights into aging changes [34].
Notably, our findings reveal significant reductions in key
system-specific disease processes, including inflammation, heart, liver, metabolic, and hormonal systems. Thisnuanced approach aligns with previous research demonstrating that vegan and plant-based diets are associatedwith lower levels of inflammatory markers [46], lower
risk of cardiovascular diseases [48, 49], reduced risk of
non-alcoholic fatty liver disease (NAFLD) [50], improve
glycemic control and other metabolic factors in individuals with type 2 diabetes [51], and regulated hormonal

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level outputs in responses such as hot flashes [52]. Thisapproach allows for a more comprehensive understanding of the impact of the studied interventions on specific facets of aging, shedding light on potential areas oftargeted intervention for promoting overall health andlongevity. The identification of these system-specificchanges contributes to a more nuanced and actionable
comprehension of the aging process, underscoring thesignificance of our results in advancing our knowledgeof interventions that may influence distinct physiologicalsystems and enhance overall well-being.
One notable difference we observed was the magnitudeof telomere length change within the vegan diet, where
qPCR-TL analysis identified a statistically significant
increase in telomere length, while PC DNAmTL exhibited an insignificant increase. While this finding is consistent with previous investigations that have reported
mixed congruency between qPCR-TL and PC DNAmTL
values [53–55], our assessment of telomere congruency between the two methods showed moderate correlation (⍴ > 0.56) and no significant difference between
PC DNAmTL and qPCR-TL, as evidenced by the Wilcoxon-rank sum and Cohen’s d tests. This suggests thatthe incongruency between the telomere length changes
observed between the two methods could be attributed
more to the different signals of telomere biology captured, such as telomere maintenance mechanisms andnot telomere length [54].
While there is no gold standard measure of biologicalaging [56], we analyzed several measures that represent
the current DNAm predictors of biological aging. Nevertheless, these measures are acknowledged to be incomplete summaries of biological changes that occur withaging and to have technical limitations [57, 58]. Treatment effects on aspects of biological aging not capturedby the DNAm measures are not included in effect estimates; measurement error due to technical limitationsof DNAm assays may bias effect estimates towards thenull. Treatment effect estimates may therefore represent
a lower bound of the true impact of vegan or omnivore
dietary intervention on biological aging.
A notable contribution of this study is the assessmentof Epigenetic Biomarker Proxies (EBP), which were previously described [14]. Firstly, the notable consistency
in significant decreases observed in both BMI-EBP and
BMI-clinical values across diet types highlights the reproducibility of BMI metrics within the epigenetic context.
Despite a slightly higher magnitude of change in BMIclinical values, the parallel trends emphasize the reliability of BMI-EBPs as reflective markers of body massindex alterations. Secondly, six EBPs exhibited divergent
alterations between the vegan and omnivore diets, shedding light on diet-specific impacts on the epigenome.

Dwaraka et al. BMC Medicine(2024) 22:301

Ergothioneine, indoleacetylglutamine, and creatinine demonstrated a noteworthy decrease in the vegan groupbut an increase in the omnivore group. Ergothioneine, apotent antioxidant guarding cells against oxidative stress,
potentially decreased in the vegan diet due to reduced
intake from sources like mushrooms and certain grains[44]. Indoleacetylglutamine, derived from tryptophan,showcased elevated levels in the omnivore diet and a
decline in the vegan diet, possibly mirroring the distinct
abundance of protein-rich foods in each diet. The analogous patterns in creatinine, a marker of muscle metabolism, might also be linked to variations in protein intake
and muscle turnover between the two diets. Conversely,
serine, 1-margaroyl-GPE(17:0), and 4-acetamidophenol
saw a significant rise in the vegan group but a decrease in
the omnivore group. Serine, a non-essential amino acidabundant in plant sources, such as soybeans and nuts,
likely increased on the vegan diet due to elevated consumption. The opposite trends in 1-margaroyl-GPE(17:0),
a relatively novel metabolite predicted to function as aglycerophospholipid involved in cellular membranes andsignaling pathways, suggest diet-induced variations inmembrane composition and function. 4-acetamidophenol, a derivative of paracetamol widely used in analgesic
and antipyretic medications, may reflect increased usage
in the vegan compared to the omnivore group. Further
studies are needed to identify the health implications ofthese changes and whether specific dietary components
are responsible for them. Thirdly, the analysis of thepreviously published EpiScores provided insights into
the potential of DNA methylation markers for predicting complex physiological and behavioral traits influenced by diet [37]. While seven EpiScores showed small
effect changes exclusively in the vegan group and six
exhibited exclusive significance in the omnivore group,
the failure to achieve the corrected p-value provides anavenue for further interrogation of their utility in interventional data. However, the EpiScores identified by theuncorrected p-value threshold do act as targets for further assessment in clinical and lab-based protein studies.
Nevertheless, the significant changes in EBP values highlight the potential of DNA methylation-based surrogate
markers in delineating diet-related impacts on complex
traits. This underscores the necessity for further exploration to refine and validate these markers for their predictive utility.
Several metabolites EBPs exhibited noteworthy changes,
providing insights into differences and commonalities
of diet response between the two groups. Among the
top markers showing significant alterations in the vegangroup, C-reactive protein (CRP), deoxycholic acid glucuronide, and spermidine stood out. A decrease in predicted

Page 14 of 19

CRP levels suggests a potential reduction in systemic
inflammation. Spermidine, a polyamine associated withvarious health benefits, demonstrated an increase, potentially indicating an increased intake of vegetables like soy,legumes, and mushrooms. Deoxycholic acid glucuronide,a bile acid metabolite, displayed a decrease, suggesting
an expected potential reduction in bile acid metabolism
in response to a reduced intake of animal fat. Additionally, the vegan group demonstrated significant changes
in other markers, such as N-acetyl-cadaverine and carnitine. Whereas the elevated levels of N-acetyl-cadaverine decreased as expected, given that this marker isassociated with amino acid fermentation in the gut, theincrease in carnitine levels contradicts the anticipateddecrease in response to a vegan diet, since carnitine ismainly derived from meat and dairy products [59].
Several metabolites exhibited significant decreases
in both diet groups, pointing to shared metabolic
responses across diverse dietary patterns. Both salicylate, a component found in various plant foods, andits metabolite salicyluric glucuronide, demonstrated
a reduction in both groups potentially reflecting adecrease in salicylate rich food such as legumes (e.g.,
lentils, beans), vegetables (e.g., cauliflowers, pickled
vegetables), and fruits (e.g., strawberries, plums, watermelons). Reductions in quinate, a compound derived
from the metabolism of coffee polyphenols [60, 61] and10-undecenoate (11:1n1), a fatty acid related to butter intake [62, 63], suggest potential reduction in coffee and butter intake, respectively. Interestingly, bothgroups exhibited a decrease in predicted body massindex (BMI), which is consistent with the decrease in
BMI in both groups.
In the omnivore group, we observed several intriguing
shifts in key metabolic markers. The increase in tryptophan and serotonin, a neurotransmitter synthesized fromtryptophan, suggests potential impacts on mood regulation and other serotonin-mediated functions in response
to increased intake of tryptophan-rich animal protein in
the omnivore diet. Choline phosphate, a vital component in cell membrane structure, exhibited an increase,hinting at increased dietary intake from meat, fish, andeggs. Indolebutyrate, a microbial metabolite, displayed
an increase, suggesting potential shifts in gut microbial
metabolism influenced by the diverse dietary components. Adenosine, a nucleoside that promotes sleep andreduces anxiety, exhibited an increase, indicating potential changes in endogenous metabolism on an omnivore
diet [64]. These findings underscore the nuanced interplay of neurotransmitter synthesis, lipid metabolism,microbial activity, and purine metabolism associatedwith omnivorous dietary patterns.

Dwaraka et al. BMC Medicine(2024) 22:301

Previous studies have suggested vegan diets associatedwith lower T2D risk [40, 41]. Interestingly, our investigation into T2D risk-associated methylation loci revealed
that the vegan diet led to increased methylation in
ABCG1 and PHOSPHO1, which provided relatively conflicting results; increase in ABCG1 indicates a reduced
T2D risk, which is contradicted with the increase in
PHOSPHO1, which indicates increased T2D risk. These
results call for the need to develop disease-specific epigenetic predictors for T2D risk which go beyond single
loci risk predictors, to potential multi-loci risk predictors
exhibit significant association to disease risk.
Finally, the exploration of global DNA methylation patterns across the entire epigenome revealed significant
differences between the vegan and omnivore cohorts,
and identified 607 and 494 differentially methylated loci
(DMLs) across the genome, respectively. Notably, themodels accounted for potential confounding factors such
as BMI, age, and sex, making it likely that these DMLs are
more closely associated with the diet change. This comprehensive epigenome-wide analysis aligns with a growing body of literature examining the epigenetic effects
of different dietary patterns [65–67]. When we analyzed each diet group independently, we observed 322
hypomethylated probes in the vegan diet and 185 in theomnivore diet. These CpG sites represent the epigenetic
targets that changed during the trial, but independent ofdiet. However, to compare the evolution of each of thetwin pairs, we compared the week 8 time point for thoseindividuals in the vegan diet and those in the omnivore
diet. This analysis unraveled 980 DMLs, with 317 demonstrating higher methylation in the vegan group and663 in the omnivore group. Using the significant CpG
sites from the twin-pair comparison at week 8, we performed an enrichment analysis to elucidate the biologicalrelevance of these methylation patterns. Hypermethylated sites in vegans revealed enrichment of paracrine
signaling, response to beta-amyloid, neuron apoptosis,
and developmental processes. These findings imply thata vegan diet may influence pathways associated withcellular communication, neuroprotective mechanisms,
and development. In contrast, hypermethylation inomnivores was linked to cell cycle regulation, genomic
imprinting, cytosolic calcium ion transport, and cellularresponse to alcohol. This suggests that an omnivorous
diet may impact pathways related to cell division, genetic
regulation, cellular signaling, and responses to environmental stimuli. These insights contribute to a deeper
understanding of how diet can impact the epigenome
and, consequently, influence various aspects of cellular
activity and health outcomes. Future investigations linking the epigenetic sites identified here in the context ofgene expression may identify gene regulatory networks

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altered due to diet, further providing a molecular perspective in nutrition and diet.
Our exploratory longitudinal differential methylation
analyses were focused on identifying candidate DNA
methylation loci associated with 8 weeks of a vegan or
herbivore diet. Hence, we utilized a more stringent P
value cutoff of less than 0.001 which has been utilizedby other EWAS studies [68, 69]. Our differential methylation analyses also controlled for twin structure andother potential confounding factors of age, sex, BMI,
batch, immune cell composition, and accounted for the
repeated measures by considering twin pairs as a random effect. However, this approach may have identified
DML by chance and is a limitation of this approach compared to more stringent false discovery rate correction of
all CpG loci. Due to the limited sample size of our study,
when p-values were adjusted for multiple correction
using the false-discovery rate method as has been utilized
in large epigenome-wide association studies, in all threecomparisons, this approach appeared too conservative
as no DMLs were identified. Future studies are needed tovalidate DML associated with vegan and herbivore diets.
It is crucial to acknowledge that the observed epigenetic age and biomarker differences between the veganand omnivore groups may be predominantly attributed
to the variations in weight loss rather than solely reflecting the distinct dietary compositions. Throughout the“Food Delivery” phase, the vegan group consumed ~ 200
calories less per day than their omnivorous counterparts,
resulting in an average weight loss of 2 kg greater thanthe omnivore group by the end of the 8-week intervention. Extensive population studies and Mendelian randomization analyses have underscored the impact of BMIchanges on inducing epigenetic alterations linked to metabolic health [70, 71]. However, it should be noted thatwhile we saw significant decreases in both clinical-BMI
and EBP-BMI values, only the vegan cohort exhibited
significant reductions in epigenetic age. This calls for anuanced interpretation of our findings and emphasizes
the need for future investigations to disentangle the complex interrelationships between dietary factors, weight
dynamics, and epigenetic modifications.
While our study provides valuable insights into theshort-term effects of weight loss on two different dietson epigenetic markers, it is important to acknowledge
that the long-term impact of a vegan diet on epigenetic
processes may carry adverse effects in the absenceof sufficient intake of crucial vitamins and nutrients
essential for supporting these intricate molecular reactions. In particular, all vegans and a substantial portion of vegetarians, if not supplemented, are at risk ofdeveloping vitamin B12 deficiency, resulting in elevated levels of homocysteine—an established marker

Dwaraka et al. BMC Medicine(2024) 22:301

of dysfunctional methylation associated with increased
cardiovascular risk, including coronary artery disease(CAD) and heightened stroke susceptibility [72–74].
Vitamin B12 deficiency has been implicated in disease-related epigenetic alterations in both animals
and humans [75–78]. In our cohort, the vegan groupexhibited a lower intake of vitamin B12, althoughserum vitamin B12 levels did not demonstrate statistical differences compared to omnivores at the 8-week
mark, likely due to preserved stores [21]. It is crucial
to emphasize that long-term adherence to vegan diets
typically necessitates vitamin B12 supplementation
to mitigate the risk of deficiency and its consequential impact on epigenetic processes. Furthermore,
the vegan cohort exhibited lower caloric intake, consumed less saturated fats, more polyunsaturated fats,
and more fiber than the omnivorous group, suggesting these as the potential drivers of age reductions,rather than the vegan diet only [21]. This highlights
the imperative role of nutritional considerations in
optimizing the health outcomes associated with plantbased dietary choices. Within the context of theselimitations, our findings have implications for future
geroscience research. Aging biology research has identified multiple therapies with the potential to improvehealthy lifespan in humans. A barrier to advancing thetranslation of these therapies through human trials isthat intervention studies run for months or years, but
human aging takes decades to cause disease [79–81].
We also acknowledge the potential for differences inbehavior and lifestyle factors which may have impacted
the study findings here. As previously described (21,online Supplement 2, eAppendix), majority of factors
which may alter methylation changes were controlled
for among the individuals who participated in the trial:routine dietary checks throughout the duration of thetrail, a fixed checklist for diet adherence, and assessment of diet adherence at the end of each 4-weekphase. In one sensitivity analysis which identified
non-normal changes in a group of twins featured in adocumentary compared to the rest of the group. These
analyses identified differences in TMAO levels in a setof twins which were removed from the study, indicating a potential confounding factor of non-adherence
to the preset diet which was corrected for. A subsetof twins (N = 4) contributed to the filming of a documentary and thus were encouraged to exercise more,which may affect caloric outputs and thus epigenetic
changes [82]. While the current analyses accounted forthe large effects using a pair-wise and random effectstatistical design, minor effects in the cross-sectional
analyses may not have been accounted for.

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Conclusions
In this epigenetic analysis of an initial randomized clinical trial, we observed significant changes using epigenetic
age clocks among healthy identical twins, suggesting
short-term advantageous aging benefits for a calorierestricted vegan diet compared to an omnivorous diet.
The use of EBPs in this study showcases the potentialof epigenetic testing to provide personalized insights
into the impact of nutrition on cellular aging, enablingtargeted dietary interventions to optimize health andwell-being. Differential methylation analysis of diettype identified methylation changes unique to each diet
implementation, potentially representing methylation
markers of diet. However, it is still uncertain whether theobserved benefits may be primarily due to greater weightloss in the vegan group; thus long-term effects of unsupplemented vegan diets on epigenetic processes require
further investigation. Future research utilizing a longterm, well-controlled study design will further highlightthe complex relationships between diet, epigenetics, andhealth outcomes such as weight loss, while emphasizing
the importance of proper nutrient supplementation invegan diets.
Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03513-w.
Additional file 1: Table S1. EpiScore analysis between baseline and 8-weektest in the Stanford TWINs trial.The first column reports the EpiScore that
was assessed, followed by the unadjustedp-value, and the direction of dif‑ference of the residual values between Week 8 from Week 0 for the Vegan(columns 2 and 3), and omnivore samples (columns 4 and 5). The final twocolumns show adjusted p-values for the vegan (column 6) and omnivore
analyses (column 7). The statistical test run here was the Wilcoxon-ranksum test. The direction of change is represented as a + (representing ahigher value at Week 8 relative to Week 0) or a - (representing a lower
value at Week 8 relative to Week 0). Abbreviations: NS = not significant.
Additional file 2: Table S2. Epigenetic Biomarker Proxy (EBP) analysis
between baseline and 8-week test in the Stanford TWINs trial. The firstcolumn reports the EBP that was assessed, followed by the unadjusted
p-value, and the direction of difference of the residual values between
Week 8 from Week 0 for the Vegan (columns 2 and 3), and omnivore
samples (columns 4 and 5). The final two columns show adjusted p-values
for the vegan (column 6) and omnivore analyses (column 7). The statistical
test run here was the Wilcoxon-rank sum test. The direction of change
is represented as a + (representing a higher value at Week 8 relative to
Week 0) or a - (representing a lower value at Week 8 relative to Week 0).
Abbreviations: NS = not significant.
Additional file 3: Table S3. Excel file contains the significant results for thedifferential methylation analysis results from the EWAS time point analysis
of Week 8 vs Week 0 of the Vegan diet. Column headers of each sheet
are listed as follows: Column A represents the CpGs identified; Column Bshows the log fold change of the m-value between Week 0 vs. Week 8 foreach timepoint comparison, in which positive values are higher methyla‑tion at week 8 relative to week 0; Column C shows the average M-value
for the CpG; Column D reports the t-statistic; Column E reports theunadjusted p-value; Column F reports the false-discovery rate (FDR) cor‑rected p-value; Column G reports the B value outputted from limma; andColumn H reports the gene ID overlapping the specific CpG loci. Table S4.

Dwaraka et al. BMC Medicine(2024) 22:301

Excel file contains the significant results for the differential methylation
analysis results from the EWAS time-point analysis of Week 8 vs Week 0of the Omnivore diet. Column headers of each sheet are listed as follows:
Column A represents the CpGs identified; Column B shows the log foldchange of the m-value between Week 0 vs. Week 8 for each timepointcomparison, in which positive values are higher methylation at week 8
relative to week 0; Column C shows the average M-value for the CpG;
Column D reports the t-statistic; Column E reports the unadjusted p-value;
Column F reports the false-discovery rate (FDR) corrected p-value; Column
G reports the B value outputted from limma; and Column H reports thegene ID overlapping the specific CpG loci. Table S5. Excel file contains thesignificant results for the differential methylation analysis results from the
EWAS time-point analysis of the Week 8 Vegan compared to the Week 8of the Omnivore diet. Column headers of each sheet are listed as follows:
Column A represents the CpGs identified; Column B shows the log foldchange of the m-value between the Vegan vs. Omnivore at Week 8, in
which positive values are higher methylation in the vegans relative toomnivores; Column C shows the average M-value for the CpG; Column Dreports the t-statistic; Column E reports the unadjusted p-value; Column
F reports the false-discovery rate (FDR) corrected p-value; Column G
reports the B value outputted from limma; and Column H reports thegene ID overlapping the specific CpG loci. Table S6. Excel file contains thesignificant results for the differential methylation analysis results from the
EWAS time-point analysis of the Week 0 Vegan compared to the Week 0of the Omnivore diet. Column headers of each sheet are listed as follows:
Column A represents the CpGs identified; Column B shows the log foldchange of the m-value between the Vegan vs. Omnivore at Week 8, in
which positive values are higher methylation in the vegans relative toomnivores; Column C shows the average M-value for the CpG; Column Dreports the t-statistic; Column E reports the unadjusted p-value; Column F
reports the false-discovery rate (FDR) corrected p-value; Column G reports
the B value outputted from limma; and Column H reports the gene ID
overlapping the specific CpG loci.
Additional file 4: Table S7. GREAT results for DMLs hypermethylated in
Vegan samples at 8 weeks, compared to Omnivore samples. Columns
presented are as follows: Column A represents the GO ID of the term
identified; Column B exhibits the name of the GO ID; Column C exhibits
the total number of genes matched from the list of CpGs; Column D
represents the hypergeometric coefficient expected; Column E represents
the hypergeometric coefficient observed from the CPG set; Column G
represents the fold enrichment of the hypergeometric value; Column G
represents the raw hypergeometric testp-value; Column H represents theadjusted hypergeometric test p-value (BH); and Column I represents theclass of gene ontology (GO) term - Molecular Function (MF), Biological
Process (BP), and Cellular Component (CC). Table S8. GREAT results for
DMLs hypomethylated in Vegan samples at 8 weeks, compared to Omni‑
vore samples. Columns presented are as follows: Column A represents
the GO ID of the term identified; Column B exhibits the name of the GO
ID; Column C exhibits the total number of genes matched from the listof CpGs; Column D represents the hypergeometric coefficient expected;
Column E represents the hypergeometric coefficient observed from the
CPG set; Column G represents the fold enrichment of the hypergeometricvalue; Column G represents the raw hypergeometric test p-value; Column
H represents the adjusted hypergeometric test p-value (BH); and Column
I represents the class of gene ontology (GO) term - Molecular Function(MF), Biological Process (BP), and Cellular Component (CC).
Acknowledgements
We are grateful to all participants and researchers who took part in this study.
Authors’ contributions
VBD, LA, and CDG had full access to the data and verified the data integrity
and accuracy of the analysis. Writing of manuscript—VBD, LA, MJC, AL, RS, ERA.
Conceived and designed the study, and provided funding—VBD, CDG, JLS.
Sample Selection, Patient Recruitment, and sample analysis—TH, JLR, CDG.
Sample processing and Methylation Data generation—TLM, HW. Telomere
data contribution—MMC, JLS, EDS. Data processing, normalization, epigenetic
clock quantification, statistical analysis, EWAS analysis—VBD. Results inter‑
pretation—VBD, LA, MJC, NCG. Figure generation—VBD, NCG, AL, LT. Edited

Page 17 of 19

and revised manuscript—all authors. All authors read and approved the final
manuscript.
Funding
This study was funded in part by the Vogt Foundation (JLR, TH, JLS, and CDG).
Availability of data and materials
The data that support the findings of this study are not publicly available due
to protection of patient data in accordance to maintaining HIPAA compliance.
However, the corresponding authors can provide the data upon reasonable
request after signing a Data Use Agreement.

Declarations
Ethics approval and consent to participate
Procedures adhered to the ethical standards of the Helsinki Declaration,
approved by the Stanford University Human Subjects Committee (IRB protocol
63955, approved March 9, 2022). Written informed consent was obtained fromall participants.
Consent for publication
Not applicable.
Competing interests
Dr. Dwaraka, Dr. Carreras-Gallo, Aaron Lin, Logan Turner, Dr. Mendez, Hannah
Went, and Ryan Smith are all employees of TruDiagnostic Inc. Dr Gardner
reported receiving funding from Beyond Meat outside of the submitted
work. Dr J. L. Sonnenburg is a Chan Zuckerberg Biohub investigator. No otherdisclosures were reported.
Author details
1
TruDiagnostic, Inc, 881 Corporate Dr, Lexington, KY 40503, USA. 2 Stanford
Prevention Research Center, Department of Medicine, School of Medicine,
Stanford University, 3180 Porter Dr, Palo Alto, Stanford, CA 94305, USA. 3 Seattle
Children’s Research Institute, Seattle, WA 98101, USA. 4 Department of Micro‑
biology and Immunology, School of Medicine, Stanford University, Stanford
University, Palo Alto, CA, USA. 5 Department of Medicine, Division of Infectious
Diseases, Weill Cornell Medicine, New York, NY, USA. 6 Chan Zuckerberg Bio‑hub, San Francisco, CA, USA. 7 Center for Human Microbiome Studies, Stanford
University School of Medicine, Stanford, CA, USA.
Received: 7 February 2024 Accepted: 2 July 2024

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